arXiv AI
MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Aug 18
BrainLinear: A Linear Model for Brain Network Analysis in Sparse Tangent Subspaces
arXiv:2608. 15266v1 Announce Type: cross Abstract: Functional connectome analysis examines brain-region interactions to understand and identify disorders such as autism spectrum disorder and Alzheimer's disease.
arXiv Machine Learning
Jun 30
BrainRiem: Riemannian Prototype Learning for Source-Free Cross-Site Brain Network Diagnosis
arXiv:2606. 29200v1 Announce Type: new Abstract: Multi-site functional MRI (fMRI) studies are essential for robust neuropsychiatric diagnosis yet suffer severe domain shifts from scanner heterogeneity, demographics, and site-specific acquisition protocols.
arXiv Machine Learning
Sep 1
Benchmarking External Generalization of SPD Matrix Learning for Resting-State fMRI Connectome Prediction
arXiv:2608.30418v1 Announce Type: cross Abstract: Resting-state functional magnetic resonance imaging (rs-fMRI) functional connectivity (FC) matrices are widely used for individual-level prediction,...
arXiv Machine Learning
Aug 11
A continually expandable foundation model for brain MRI
arXiv:2608. 08319v1 Announce Type: cross Abstract: Brain magnetic resonance imaging (MRI) is central to neuroscience and clinical assessment, but models are commonly developed for individual diseases, populations or imaging protocols.
arXiv Machine Learning
Oct 9
BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation
BrainATCL is an unsupervised, nonparametric framework that learns adaptive temporal brain connectivity from resting‑state fMRI data. It dynamically adjusts the lookback window for each snapshot based on newly added edges and encodes graph sequences with a GINE‑Mamba2 backbone, incorporating brain‑structure and function‑informed edge attributes. The method is evaluated on functional link prediction and age estimation, showing superior performance and strong generalization, even across sessions.
arXiv AI
Jun 15
GMN4AD: Graph Matching Network for Alzheimer's Disease Diagnosis with Test-Time Domain Adaptation using Multi-centered Structure Magnetic Resonance Imaging
arXiv:2606. 13919v1 Announce Type: cross Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that affects millions of older adults, with prevalence expected to rise significantly in the coming years.